






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


snake_case     					=		logging.get_logger(__name__)

snake_case     					=		{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

snake_case     					=		{
    """vocab_file""": {
        """junnyu/roformer_chinese_small""": """https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt""",
        """junnyu/roformer_chinese_base""": """https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt""",
        """junnyu/roformer_chinese_char_small""": (
            """https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt"""
        ),
        """junnyu/roformer_chinese_char_base""": (
            """https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt"""
        ),
        """junnyu/roformer_small_discriminator""": (
            """https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt"""
        ),
        """junnyu/roformer_small_generator""": (
            """https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt"""
        ),
    }
}

snake_case     					=		{
    """junnyu/roformer_chinese_small""": 1_536,
    """junnyu/roformer_chinese_base""": 1_536,
    """junnyu/roformer_chinese_char_small""": 512,
    """junnyu/roformer_chinese_char_base""": 512,
    """junnyu/roformer_small_discriminator""": 128,
    """junnyu/roformer_small_generator""": 128,
}


snake_case     					=		{
    """junnyu/roformer_chinese_small""": {"""do_lower_case""": True},
    """junnyu/roformer_chinese_base""": {"""do_lower_case""": True},
    """junnyu/roformer_chinese_char_small""": {"""do_lower_case""": True},
    """junnyu/roformer_chinese_char_base""": {"""do_lower_case""": True},
    """junnyu/roformer_small_discriminator""": {"""do_lower_case""": True},
    """junnyu/roformer_small_generator""": {"""do_lower_case""": True},
}


class 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase       ):
     '''simple docstring'''


     UpperCamelCase_				:		Optional[Any]								=						VOCAB_FILES_NAMES
     UpperCamelCase_				:		int								=						PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase_				:		Tuple								=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCamelCase_				:		List[Any]								=						PRETRAINED_INIT_CONFIGURATION
     UpperCamelCase_				:		Any								=						RoFormerTokenizer


     def __init__(					self							:						Tuple       ,			UpperCAmelCase_							:						List[Any]=None       ,			UpperCAmelCase_							:						List[Any]=None       ,			UpperCAmelCase_							:						Optional[Any]=True       ,			UpperCAmelCase_							:						List[str]="[UNK]"       ,			UpperCAmelCase_							:						Any="[SEP]"       ,			UpperCAmelCase_							:						Any="[PAD]"       ,			UpperCAmelCase_							:						List[str]="[CLS]"       ,			UpperCAmelCase_							:						str="[MASK]"       ,			UpperCAmelCase_							:						Any=True       ,			UpperCAmelCase_							:						Optional[Any]=None       ,			**UpperCAmelCase_							:						List[str]       ,			):
       super().__init__(
           UpperCAmelCase_       ,			tokenizer_file=UpperCAmelCase_       ,			do_lower_case=UpperCAmelCase_       ,			unk_token=UpperCAmelCase_       ,			sep_token=UpperCAmelCase_       ,			pad_token=UpperCAmelCase_       ,			cls_token=UpperCAmelCase_       ,			mask_token=UpperCAmelCase_       ,			tokenize_chinese_chars=UpperCAmelCase_       ,			strip_accents=UpperCAmelCase_       ,			**UpperCAmelCase_       ,			)

       SCREAMING_SNAKE_CASE							:   int						    =     json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
       if (
           pre_tok_state.get("lowercase"       ,			UpperCAmelCase_   ) != do_lower_case
           or pre_tok_state.get("strip_accents"       ,			UpperCAmelCase_   ) != strip_accents
       ):
         SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     getattr(UpperCAmelCase_       ,			pre_tok_state.pop("type"   )   )
         SCREAMING_SNAKE_CASE							:   Any						    =     do_lower_case
         SCREAMING_SNAKE_CASE							:   List[str]						    =     strip_accents
         SCREAMING_SNAKE_CASE							:   Tuple						    =     pre_tok_class(**UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   int						    =     do_lower_case


     def __getstate__(					self							:						Union[str, Any]   ):
       SCREAMING_SNAKE_CASE							:   Any						    =     self.__dict__.copy()
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     BertPreTokenizer()
       return state


     def __setstate__(					self							:						Tuple       ,			UpperCAmelCase_							:						Union[str, Any]   ):
       SCREAMING_SNAKE_CASE							:   Dict						    =     d
       SCREAMING_SNAKE_CASE							:   Dict						    =     self.__dict__["_tokenizer"].get_vocab()
       SCREAMING_SNAKE_CASE							:   Any						    =     PreTokenizer.custom(JiebaPreTokenizer(UpperCAmelCase_   )   )


     def 					_A							(					self							:						str       ,			UpperCAmelCase_							:						Optional[int]       ,			UpperCAmelCase_							:						Union[str, Any]=None   ):
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     [self.cls_token_id] + token_ids_a + [self.sep_token_id]

       if token_ids_a:
         output += token_ids_a + [self.sep_token_id]

       return output


     def 					_A							(					self							:						Tuple       ,			UpperCAmelCase_							:						List[int]       ,			UpperCAmelCase_							:						Optional[List[int]] = None   ):
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     [self.sep_token_id]
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     [self.cls_token_id]
       if token_ids_a is None:
         return len(cls + token_ids_a + sep   ) * [0]
       return len(cls + token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]


     def 					_A							(					self							:						Optional[int]       ,			UpperCAmelCase_							:						str       ,			UpperCAmelCase_							:						Optional[str] = None   ):
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self._tokenizer.model.save(UpperCAmelCase_       ,			name=UpperCAmelCase_   )
       return tuple(UpperCAmelCase_   )






     def 					_A							(					self							:						Tuple       ,			UpperCAmelCase_							:						List[Any]       ,			UpperCAmelCase_							:						Any=None       ,			UpperCAmelCase_							:						str=None       ,			UpperCAmelCase_							:						Any=False       ,			**UpperCAmelCase_							:						str       ,			):
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     BertPreTokenizer()
       return super().save_pretrained(UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_       ,			**UpperCAmelCase_   )








import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Optional[int]		,    _lowerCamelCase					:     List[Any]		,    _lowerCamelCase					:     Optional[Any])   ->       Dict:






							'''simple docstring'''





							__UpperCamelCase      :  str       =   BertConfig.from_json_file(_lowerCamelCase)
							print(F'Building PyTorch model from configuration: {config}')
							__UpperCamelCase      :  Optional[Any]       =   BertForPreTraining(_lowerCamelCase)

							# Load weights from tf checkpoint
							load_tf_weights_in_bert(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase)

							# Save pytorch-model
							print(F'Save PyTorch model to {pytorch_dump_path}')
							torch.save(model.state_dict()		,    _lowerCamelCase)


if __name__ == "__main__":
		lowercase    :   Union[str, Any]						=			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--bert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained BERT model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		lowercase    :   Any						=			parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)